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3 Commits

Author SHA1 Message Date
Devin AI
b01e4e29cf fix: use LLM model name in telemetry span
Co-Authored-By: Joe Moura <joao@crewai.com>
2025-02-09 23:33:38 +00:00
Devin AI
8c5b02b974 chore: update test cassettes and dependencies
Co-Authored-By: Joe Moura <joao@crewai.com>
2025-02-09 23:32:17 +00:00
Devin AI
15d9308acb feat: enable custom LLM support for Crew.test()
Co-Authored-By: Joe Moura <joao@crewai.com>
2025-02-09 23:31:28 +00:00
5 changed files with 277 additions and 98 deletions

View File

@@ -16,6 +16,8 @@ from pydantic import (
field_validator,
model_validator,
)
from crewai.llm import LLM
from pydantic_core import PydanticCustomError
from crewai.agent import Agent
@@ -1076,18 +1078,32 @@ class Crew(BaseModel):
self,
n_iterations: int,
openai_model_name: Optional[str] = None,
llm: Optional[Union[str, LLM]] = None,
inputs: Optional[Dict[str, Any]] = None,
) -> None:
"""Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures."""
"""Test and evaluate the Crew with the given inputs for n iterations concurrently using concurrent.futures.
Args:
n_iterations: Number of iterations to run the test
openai_model_name: Name of OpenAI model to use (deprecated, use llm instead)
llm: LLM instance or model name to use for evaluation
inputs: Optional inputs to pass to the crew
"""
test_crew = self.copy()
# Convert string to LLM instance if needed
if isinstance(llm, str):
llm = LLM(model=llm)
elif openai_model_name:
llm = LLM(model=openai_model_name)
self._test_execution_span = test_crew._telemetry.test_execution_span(
test_crew,
n_iterations,
inputs,
openai_model_name, # type: ignore[arg-type]
) # type: ignore[arg-type]
evaluator = CrewEvaluator(test_crew, openai_model_name) # type: ignore[arg-type]
getattr(llm, "model", openai_model_name),
)
evaluator = CrewEvaluator(test_crew, llm)
for i in range(1, n_iterations + 1):
evaluator.set_iteration(i)

View File

@@ -1,4 +1,5 @@
from collections import defaultdict
from typing import Union
from pydantic import BaseModel, Field
from rich.box import HEAVY_EDGE
@@ -6,6 +7,7 @@ from rich.console import Console
from rich.table import Table
from crewai.agent import Agent
from crewai.llm import LLM
from crewai.task import Task
from crewai.tasks.task_output import TaskOutput
from crewai.telemetry import Telemetry
@@ -32,9 +34,9 @@ class CrewEvaluator:
run_execution_times: defaultdict = defaultdict(list)
iteration: int = 0
def __init__(self, crew, openai_model_name: str):
def __init__(self, crew, llm: Union[str, LLM]):
self.crew = crew
self.openai_model_name = openai_model_name
self.llm = llm if isinstance(llm, LLM) else LLM(model=llm)
self._telemetry = Telemetry()
self._setup_for_evaluating()
@@ -51,7 +53,7 @@ class CrewEvaluator:
),
backstory="Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed",
verbose=False,
llm=self.openai_model_name,
llm=self.llm,
)
def _evaluation_task(
@@ -181,7 +183,7 @@ class CrewEvaluator:
self.crew,
evaluation_result.pydantic.quality,
current_task._execution_time,
self.openai_model_name,
getattr(self.llm, "model", None),
)
self.tasks_scores[self.iteration].append(evaluation_result.pydantic.quality)
self.run_execution_times[self.iteration].append(

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@@ -14,6 +14,7 @@ from crewai.agent import Agent
from crewai.agents.cache import CacheHandler
from crewai.crew import Crew
from crewai.crews.crew_output import CrewOutput
from crewai.llm import LLM
from crewai.memory.contextual.contextual_memory import ContextualMemory
from crewai.process import Process
from crewai.task import Task
@@ -1123,7 +1124,7 @@ def test_kickoff_for_each_empty_input():
assert results == []
@pytest.mark.vcr(filter_headers=["authorization"])
@pytest.mark.vcr(filter_headeruvs=["authorization"])
def test_kickoff_for_each_invalid_input():
"""Tests if kickoff_for_each raises TypeError for invalid input types."""
@@ -2812,10 +2813,11 @@ def test_conditional_should_execute():
@mock.patch("crewai.crew.CrewEvaluator")
@mock.patch("crewai.crew.Crew.copy")
@mock.patch("crewai.crew.Crew.kickoff")
def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
def test_crew_testing_function_with_openai_model_name(kickoff_mock, copy_mock, crew_evaluator):
"""Test backward compatibility with openai_model_name parameter."""
task = Task(
description="Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.",
expected_output="5 bullet points with a paragraph for each idea.",
description="Test task",
expected_output="Test output",
agent=researcher,
)
@@ -2824,20 +2826,87 @@ def test_crew_testing_function(kickoff_mock, copy_mock, crew_evaluator):
tasks=[task],
)
# Create a mock for the copied crew
copy_mock.return_value = crew
n_iterations = 2
crew.test(n_iterations, openai_model_name="gpt-4o-mini", inputs={"topic": "AI"})
# Ensure kickoff is called on the copied crew
kickoff_mock.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)
crew_evaluator.assert_has_calls(
[
mock.call(crew, "gpt-4o-mini"),
mock.call(crew, mock.ANY), # ANY because we convert to LLM instance
mock.call().set_iteration(1),
mock.call().set_iteration(2),
mock.call().print_crew_evaluation_result(),
]
)
@mock.patch("crewai.crew.CrewEvaluator")
@mock.patch("crewai.crew.Crew.copy")
@mock.patch("crewai.crew.Crew.kickoff")
def test_crew_testing_function_with_llm_instance(kickoff_mock, copy_mock, crew_evaluator):
"""Test using LLM instance parameter."""
task = Task(
description="Test task",
expected_output="Test output",
agent=researcher,
)
crew = Crew(
agents=[researcher],
tasks=[task],
)
copy_mock.return_value = crew
llm = LLM(model="gpt-4o-mini")
n_iterations = 2
crew.test(n_iterations, llm=llm, inputs={"topic": "AI"})
kickoff_mock.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)
crew_evaluator.assert_has_calls(
[
mock.call(crew, llm),
mock.call().set_iteration(1),
mock.call().set_iteration(2),
mock.call().print_crew_evaluation_result(),
]
)
@mock.patch("crewai.crew.CrewEvaluator")
@mock.patch("crewai.crew.Crew.copy")
@mock.patch("crewai.crew.Crew.kickoff")
def test_crew_testing_function_with_llm_string(kickoff_mock, copy_mock, crew_evaluator):
"""Test using LLM string parameter."""
task = Task(
description="Test task",
expected_output="Test output",
agent=researcher,
)
crew = Crew(
agents=[researcher],
tasks=[task],
)
copy_mock.return_value = crew
n_iterations = 2
crew.test(n_iterations, llm="gpt-4o-mini", inputs={"topic": "AI"})
kickoff_mock.assert_has_calls(
[mock.call(inputs={"topic": "AI"}), mock.call(inputs={"topic": "AI"})]
)
crew_evaluator.assert_has_calls(
[
mock.call(crew, mock.ANY), # ANY because we don't care about the LLM instance details
mock.call().set_iteration(1),
mock.call().set_iteration(2),
mock.call().print_crew_evaluation_result(),
@@ -3125,4 +3194,4 @@ def test_multimodal_agent_live_image_analysis():
# Verify we got a meaningful response
assert isinstance(result.raw, str)
assert len(result.raw) > 100 # Expecting a detailed analysis
assert "error" not in result.raw.lower() # No error messages in response
assert "error" not in result.raw.lower() # No error messages in response

68
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